Explain how you would design an end-to-end data pipeline using AWS Glue, from S3 to a data warehouse, including staging, transformation, and final loading.

I would start by creating an S3 bucket as the landing zone for raw data. A Glue crawler would catalog the data, generating a Glue Data Catalog. For staging, I’d use Glue ETL jobs written in Python or Scala to clean and enrich the data, writing the results to a separate S3 bucket in Parquet format for columnar storage and compression. The ETL job would also write metadata to the Data Catalog. For the final load, I’d use a Glue job to push the transformed data into Amazon Redshift via the COPY command, leveraging Redshift Spectrum if the data volume is large. I’d schedule the jobs with Glue Workflows to orchestrate dependencies and use AWS Step Functions for error handling and retries. Monitoring would be set up with CloudWatch metrics and alerts, and I’d enable Glue job bookmarking to avoid reprocessing. This architecture provides scalability, cost efficiency, and robust error recovery.